

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
a_      =     logging.get_logger(__name__)

a_      =     {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
a_      =     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a):
	for attribute in key.split("."):
		SCREAMING_SNAKE_CASE :   List[Any]					  =							getattr(_a				,      _a)

	if weight_type is not None:
		SCREAMING_SNAKE_CASE :   str					  =							getattr(_a				,      _a).shape
	else:
		SCREAMING_SNAKE_CASE :   Dict					  =							hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
		    f" {value.shape} for {full_name}")

	if weight_type == "weight":
		SCREAMING_SNAKE_CASE :   Any					  =							value
	elif weight_type == "weight_g":
		SCREAMING_SNAKE_CASE :   List[Any]					  =							value
	elif weight_type == "weight_v":
		SCREAMING_SNAKE_CASE :   List[Any]					  =							value
	elif weight_type == "bias":
		SCREAMING_SNAKE_CASE :   List[Any]					  =							value
	else:
		SCREAMING_SNAKE_CASE :   Any					  =							value

	logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")

def 			lowerCamelCase__			(       _a				,      _a):
	SCREAMING_SNAKE_CASE :   int					  =							[]
	SCREAMING_SNAKE_CASE :   int					  =							fairseq_model.state_dict()

	SCREAMING_SNAKE_CASE :   int					  =							hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							False
		if "conv_layers" in name:
			load_conv_layer(
			    _a				,      _a				,      _a				,      _a				,      hf_model.config.feat_extract_norm == "group"				,      )
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							True
		else:
			for key, mapped_key in MAPPING.items():
				SCREAMING_SNAKE_CASE :   str					  =							"unispeech_sat." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
					if "layer_norm_for_extract" in name and (".".join(name.split(".")[:-1]) != key):
						# special case since naming is very similar
						continue
					SCREAMING_SNAKE_CASE :   Optional[int]					  =							True
					if "*" in mapped_key:
						SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							name.split(_a)[0].split(".")[-2]
						SCREAMING_SNAKE_CASE :   Dict					  =							mapped_key.replace("*"				,      _a)
					if "weight_g" in name:
						SCREAMING_SNAKE_CASE :   Any					  =							"weight_g"
					elif "weight_v" in name:
						SCREAMING_SNAKE_CASE :   List[str]					  =							"weight_v"
					elif "bias" in name:
						SCREAMING_SNAKE_CASE :   str					  =							"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						SCREAMING_SNAKE_CASE :   Any					  =							"weight"
					else:
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							None
					set_recursively(_a				,      _a				,      _a				,      _a				,      _a)
				continue
		if not is_used:
			unused_weights.append(_a)

	logger.warning(f"Unused weights: {unused_weights}")

def 			lowerCamelCase__			(       _a				,      _a				,      _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							full_name.split("conv_layers.")[-1]
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							name.split(".")
	SCREAMING_SNAKE_CASE :   Any					  =							int(items[0])
	SCREAMING_SNAKE_CASE :   Any					  =							int(items[1])

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.")
			SCREAMING_SNAKE_CASE :   Tuple					  =							value
			logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.")
			SCREAMING_SNAKE_CASE :   int					  =							value
			logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.")
			SCREAMING_SNAKE_CASE :   Tuple					  =							value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f"{full_name} has size {value.shape}, but"
				    f" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.")
			SCREAMING_SNAKE_CASE :   Tuple					  =							value
			logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
	else:
		unused_weights.append(_a)

@torch.no_grad()
def 			lowerCamelCase__			(       _a				,      _a				,      _a=None				,      _a=None				,      _a=True):
	if config_path is not None:
		SCREAMING_SNAKE_CASE :   Tuple					  =							UniSpeechSatConfig.from_pretrained(_a)
	else:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							UniSpeechSatConfig()

	SCREAMING_SNAKE_CASE :   List[str]					  =							""

	if is_finetuned:
		SCREAMING_SNAKE_CASE :   Dict					  =							UniSpeechSatForCTC(_a)
	else:
		SCREAMING_SNAKE_CASE :   str					  =							UniSpeechSatForPreTraining(_a)

	SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Optional[int]					  =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]				,      arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model[0].eval()

	recursively_load_weights(_a				,      _a)

	hf_wavavec.save_pretrained(_a)


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()
			parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
			parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
			parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
			parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
			parser.add_argument(
			    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
			)
			a_      =     parser.parse_args()
			convert_unispeech_sat_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)




'''simple docstring'''






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Dict					=		'▁'

lowerCAmelCase:   Dict					=		{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
}

lowerCAmelCase:   int					=		{
    'vocab_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
        ),
    },
    'spm_file': {
        'facebook/s2t-small-librispeech-asr': (
            'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
        )
    },
}

lowerCAmelCase:   Any					=		{
    'facebook/s2t-small-librispeech-asr': 1_0_2_4,
}

lowerCAmelCase:   Optional[int]					=		['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']

lowerCAmelCase:   List[Any]					=		{'mustc': MUSTC_LANGS}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       VOCAB_FILES_NAMES
    lowercase__					=       PRETRAINED_VOCAB_FILES_MAP
    lowercase__					=       MAX_MODEL_INPUT_SIZES
    lowercase__					=       ["""input_ids""", """attention_mask"""]

    lowercase__					=       []







    def __init__( self					:    int		,     __snake_case					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Optional[Any]="<s>"		,     __snake_case					:    Optional[int]="</s>"		,     __snake_case					:    Union[str, Any]="<pad>"		,     __snake_case					:    str="<unk>"		,     __snake_case					:    Dict=False		,     __snake_case					:    int=False		,     __snake_case					:    str=None		,     __snake_case					:    Optional[int]=None		,     __snake_case					:    Optional[Dict[str, Any]] = None		,     **__snake_case					:    Union[str, Any]		,     ):
      a		:   Union[str, Any]	  =	{} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=__snake_case		,     eos_token=__snake_case		,     unk_token=__snake_case		,     pad_token=__snake_case		,     do_upper_case=__snake_case		,     do_lower_case=__snake_case		,     tgt_lang=__snake_case		,     lang_codes=__snake_case		,     sp_model_kwargs=self.sp_model_kwargs		,     **__snake_case		,     )
      a		:   Tuple	  =	do_upper_case
      a		:   Optional[Any]	  =	do_lower_case

      a		:   List[str]	  =	load_json(__snake_case      )
      a		:   Dict	  =	{v: k for k, v in self.encoder.items()}
      a		:   int	  =	spm_file
      a		:   Tuple	  =	load_spm(__snake_case		,     self.sp_model_kwargs      )

      if lang_codes is not None:
        a		:   Any	  =	lang_codes
        a		:   str	  =	LANGUAGES[lang_codes]
        a		:   Tuple	  =	[F"""<lang:{lang}>""" for lang in self.langs]
        a		:   str	  =	{lang: self.sp_model.PieceToId(F"""<lang:{lang}>"""      ) for lang in self.langs}

        a		:   Optional[Any]	  =	self.lang_tokens
        a		:   Union[str, Any]	  =	tgt_lang if tgt_lang is not None else self.langs[0]

        self.set_tgt_lang_special_tokens(self._tgt_lang      )
      else:
        a		:   List[str]	  =	{}







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      return len(self.encoder      )







    @property
    def      lowercase_			( self					:    int      ):
      return self._tgt_lang







    @tgt_lang.setter
    def      lowercase_			( self					:    int		,     __snake_case					:    Optional[int]      ):
      a		:   Union[str, Any]	  =	new_tgt_lang
      self.set_tgt_lang_special_tokens(__snake_case      )







    def      lowercase_			( self					:    str		,     __snake_case					:    str      ):
      a		:   int	  =	self.lang_code_to_id[tgt_lang]
      a		:   int	  =	[lang_code_id]







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    str      ):
      return self.sp_model.encode(__snake_case		,     out_type=__snake_case      )







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    List[Any]      ):
      return self.encoder.get(__snake_case		,     self.encoder[self.unk_token]      )







    def      lowercase_			( self					:    List[Any]		,     __snake_case					:    int      ):
      return self.decoder.get(__snake_case		,     self.unk_token      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    List[str]      ):
      a		:   List[Any]	  =	[]
      a		:   List[Any]	  =	''
      for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self.all_special_tokens:
          a		:   Union[str, Any]	  =	self.sp_model.decode(__snake_case      )
          out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
          a		:   Optional[int]	  =	[]
        else:
          current_sub_tokens.append(__snake_case      )
      a		:   Tuple	  =	self.sp_model.decode(__snake_case      )
      out_string += decoded.upper() if self.do_upper_case else decoded
      return out_string.strip()







    def      lowercase_			( self					:    int		,     __snake_case					:    List[Any]		,     __snake_case					:    List[str]=None      ):
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + [self.eos_token_id]
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]







    def      lowercase_			( self					:    Optional[int]		,     __snake_case					:    List[int]		,     __snake_case					:    Optional[List[int]] = None		,     __snake_case					:    bool = False      ):

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__snake_case		,     token_ids_a=__snake_case		,     already_has_special_tokens=__snake_case      )

      a		:   Optional[int]	  =	[1] * len(self.prefix_tokens      )
      a		:   Any	  =	[1]
      if token_ids_a is None:
        return prefix_ones + ([0] * len(__snake_case      )) + suffix_ones
      return prefix_ones + ([0] * len(__snake_case      )) + ([0] * len(__snake_case      )) + suffix_ones







    def      lowercase_			( self					:    Union[str, Any]      ):
      a		:   Union[str, Any]	  =	self.encoder.copy()
      vocab.update(self.added_tokens_encoder      )
      return vocab







    def __getstate__( self					:    Any      ):
      a		:   List[str]	  =	self.__dict__.copy()
      a		:   Union[str, Any]	  =	None
      return state







    def __setstate__( self					:    str		,     __snake_case					:    Dict      ):
      a		:   Dict	  =	d

      # for backward compatibility
      if not hasattr(self		,     'sp_model_kwargs'      ):
        a		:   int	  =	{}

      a		:   Any	  =	load_spm(self.spm_file		,     self.sp_model_kwargs      )







    def      lowercase_			( self					:    str		,     __snake_case					:    str		,     __snake_case					:    Optional[str] = None      ):
      a		:   Union[str, Any]	  =	Path(__snake_case      )
      assert save_dir.is_dir(), F"""{save_directory} should be a directory"""
      a		:   Any	  =	save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
      )
      a		:   List[Any]	  =	save_dir / (
          (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
      )

      save_json(self.encoder		,     __snake_case      )

      if os.path.abspath(self.spm_file      ) != os.path.abspath(__snake_case      ) and os.path.isfile(self.spm_file      ):
        copyfile(self.spm_file		,     __snake_case      )
      elif not os.path.isfile(self.spm_file      ):
        with open(__snake_case		,     'wb'      ) as fi:
          a		:   Tuple	  =	self.sp_model.serialized_model_proto()
          fi.write(__snake_case      )

      return (str(__snake_case      ), str(__snake_case      ))
def  lowerCamelCase__			(		_A					,     _A							):
  a		:   List[Any]	  =	sentencepiece.SentencePieceProcessor(**_A							)
  spm.Load(str(_A							)							)
  return spm
def  lowerCamelCase__			(		_A							):
  with open(_A					,     'r'							) as f:
    return json.load(_A							)

def  lowerCamelCase__			(		_A					,     _A							):
  with open(_A					,     'w'							) as f:
    json.dump(_A					,     _A					,     indent=2							)